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Direct-drive wave energy converter (WEC) and buoy control algorithms have shown great potential for renewable wave energy extraction in ideal conditions. However the actual power take-off (PTO) impacts are barely considered in the WEC design. This paper highlights the demands of designing the WEC wave-to-wire control from a global point of view by studying the actual PTO impacts. A permanent magnet linear electrical machine (LEM) PTO unit is simulated and controlled to fulfill the WEC buoy control requirements. Several state of the art control algorithms, which include singular-arc (SA) control, shape-based (SB) control, model predictive control (MPC), and proportional-derivative (PD) control, are applied to maximize the wave energy production (mechanical energy). Multiple types of electrical PTOs, including ideal PTO, unlimited PTO and limited PTO, are all implemented to evaluate WEC wave-to-wire performances. Further, the PTO copper loss model and the PTO actual efficiency maps are introduced and studied to improve the electrical PTO operation efficiency. To further assess the control schemes in various wave conditions, one-year PacWave ground-truth data is applied as well. Numerical simulations are conducted using MATLAB/Simulink and the Simscape toolbox. The electrical PTO unit is composed of a LEM, an ideal inverter, and an ideal energy storage system. The results show that the actual PTO will impact the constrained controls (MPC and SB) less comapring to unconstrained controls (SA and PD). Although SB can produce the maximum energy with the limited PTOs, it is not robust for all wave conditions. At the end of the paper, the possible solutions for improving the WEC wave-to-wire performances are also provided. [ABSTRACT FROM AUTHOR]

Power distribution systems are experiencing a large-scale integration of Converter-Interfaced Distributed Energy Resources (CIDERs). This complicates the analysis and mitigation of harmonics, whose creation and propagation are facilitated by the interactions of converters and their controllers through the grid. In this paper, a method for the calculation of the so-called Harmonic Power-Flow (HPF) in three-phase grids with CIDERs is proposed. The distinguishing feature of this HPF method is the generic and modular representation of the system components. Notably, as opposed to most of the existing approaches, the coupling between harmonics is explicitly considered. The HPF problem is formulated by combining the hybrid nodal equations of the grid with the closed-loop transfer functions of the CIDERs, and solved using the Newton-Raphson method. The grid components are characterized by compound electrical parameters, which allow to represent both transposed or non-transposed lines. The CIDERs are represented by modular linear time-periodic systems, which allows to treat both grid-forming and grid-following control laws. The method’s accuracy and computational efficiency are confirmed via time-domain simulations of the CIGRÉ low-voltage benchmark microgrid. This paper is divided in two parts, which focus on the development (Part I) and the validation (Part II) of the proposed method. [ABSTRACT FROM AUTHOR]

With the widespread application of lithium-ion battery packs, it calls for efficient balancing methods to improve the performance of these battery systems. The relevant research has focused on the design of equalization circuits and control strategies while neglecting quantitative analysis and comparison of battery active equalization structures from the systematic level. In this paper, we propose an improved system-theoretic modeling approach for active equalization structures that takes into account the battery's constraints, including equalization current rate and SOC operation range. Accordingly, the mathematical models of various active equalization structures are derived that can describe their equalization dynamic behavior. Then, the analytical algorithms are developed to estimate the balancing time of active equalization structures online. Finally, extensive numerical simulations are conducted to evaluate and compare the average performance of these equalization systems in term of equalization time. It then comes up with a comprehensive comparison among them. Consequently, the Multicell-to-Multicell (MC2MC) balancing structure benefits the fast balancing speed and the lowest sensitive to initial batteries’ SOCs. The global structure has the lowest implementation cost and the series structure benefits the simplest control. Moreover, the module-based structures have faster balancing speed when each module contains about 4–6 batteries. [ABSTRACT FROM AUTHOR]

The aim of the paper is to define a limit performance of highly efficient battery electric quadricycles for urban mobility. The vehicle is employed for an energy efficiency competition in which urban concepts compete. A multi-physics (thermo-electro-mechanical) Tank To Wheels (TTW) model has been developed and validated. Given the information on the track route (track map and elevation) and on the throttle input command, the model computes the vehicle power demand and cruising speed. The model is validated by means of both indoor and outdoor experimental tests. The validated model is employed for the optimization of the transmission gear ratio and of the driving strategy to minimize the overall energy consumption on a given track. Design variables are related to the transmission gear ratio and to the throttle command profile. The algorithm aims to minimize the energy consumption, including constraints on the maximum current provided by the battery and on the maximum time available to complete the lap. Comparison with some common driving strategies confirmed the effectiveness of the proposed solution, with a foreseen 9% reduction of the vehicle overall energy demand. The single seater ultra-efficient electric vehicle, compared to other urban quadricycles, has a battery capacity ten times lower and a 50% longer driving range. [ABSTRACT FROM AUTHOR]

In this paper, compact time-domain (TD) models featuring materials with frequency-dependent electromagnetic (EM) properties are derived. The considered frequency-dependent material models include multiterm Debye and Lorentz models for the electric permittivity and the magnetic permeability and a multiterm Drude model for the electric conductivity. The TD models are based on finite-element systems in the frequency domain (FD). To render the model compact and computationally efficient, the dimension of the FD system is compressed with the help of projection-based model-order reduction. In contrast to older approaches, the TD transformation is performed on the FD model itself rather than on the transfer function. The result is a state-space representation, which may either be solved by custom time integrators or imported into commercial circuit simulators. The advantages of the new approach include provable passivity of the FD model, provable causality of the TD model, and the ability to reconstruct the transient EM fields. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for checking and enforcing passivity of behavioral reduced-order macromodels of linear time-invariant systems, whose frequency-domain (scattering) responses depend on external parameters. Such models, which are typically extracted from sampled input–output responses obtained from numerical solution of first-principle physical models, usually expressed as partial differential equations, prove extremely useful in design flows since they allow optimization, what-if or sensitivity analyses, and design centering. Starting from an implicit parameterization of both poles and residues of the model, as resulting from well-known model identification schemes based on the generalized Sanathanan–Koerner iteration, we construct a parameter-dependent skew-Hamiltonian/Hamiltonian matrix pencil. The iterative extraction of purely imaginary eigenvalues of the pencil, combined with an adaptive sampling scheme in the parameter space, is able to identify all regions in the frequency-parameter plane where local passivity violations occur. Then, a singular value perturbation scheme is set up to iteratively correct the model coefficients, until all local passivity violations are eliminated. The final result is a corrected model, which is uniformly passive throughout the parameter range. Several numerical examples demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a passive lumped model for DC cables and overhead lines, taking into account the frequency dependency of the per-unit-length parameters due to the skin and proximity effects. In comparison to distributed parameter line models, such as the Frequency-Dependent Phase model of PSCAD, the proposed model does not require line propagation delay extraction and does not impose any restrictions on the simulation timestep. As a result, for electrically short lines, the introduced model is more computationally efficient than distributed parameter models as it enables simulation using larger timesteps. The performance of the proposed model is evaluated in comparison with the PI model and the Frequency-Dependent Phase model through time-domain simulations of a DC microgrid and a radial DC distribution feeder. The studies performed in PSCAD focus on fault-induced transients. The results indicate that for electrically short lines the developed model is significantly more accurate compared to the PI model and provides similar accuracy with more computational efficiency compared to the Frequency-Dependent Phase model. [ABSTRACT FROM AUTHOR]

This paper presents a general method for building equivalent electric circuits of power transformers, including eddy current effects in windings and core. A high-frequency equivalent dual model for single- and three-phase transformers with two multilayer windings is derived from the application of the principle of duality. The model is built from elements available in circuit simulation programs, such as Electromagnetic Transients Program (EMTP)–Alternative Transients Program, EMTP-RV, PSCAD, and PSpice. The parameters of the frequency-dependent leakage inductance and winding resistance are computed with analytical formulae obtained from the solution of Maxwell’s equations that are based on the geometrical dimensions and material information. Ideal transformers are utilized to isolate the electric components (winding resistors and capacitors) from the magnetic components (inductors). The physically correct connection points for electric and magnetic components are clearly identified. The proposed methodology is successfully validated versus finite- element simulations and laboratory measurements. [ABSTRACT FROM PUBLISHER]

Single event effects (SEE) are a reliability concern for modern microelectronics. Bit corruptions can be caused by single event upsets (SEUs) in the storage cells or by sampling single event transients (SETs) from a logic path. An accurate prediction of soft error susceptibility from SETs requires good models to convert collected charge into compact descriptions of the current injection process. This paper describes a simple, yet effective, method to model the current waveform resulting from a charge collection event for SET circuit simulations. The model uses two double-exponential current sources in parallel, and the results illustrate why a conventional model based on one double-exponential source can be incomplete. A small set of logic cells with varying input conditions, drive strength, and output loading are simulated to extract the parameters for the dual double-exponential current sources. The parameters are based upon both the node capacitance and the restoring current (i.e., drive strength) of the logic cell. [ABSTRACT FROM AUTHOR]

Conventional model-order reduction techniques used in circuit simulation guarantee the passivity and, consequently, the stability of the reduced circuit provided that the original circuit is passive. This excludes a large class of circuits that are stable but not necessarily passive. In this paper, an algorithm is described that guarantees the stability of the reduced circuit by construction. Numerical examples are presented that validate the efficiency and accuracy of the proposed method. [ABSTRACT FROM PUBLISHER]

With ever increasing design complexities, traditional cycle-accurate or instruction-set simulations are often too slow or too inaccurate for system prototyping in early design stages. As an alternative, host-compiled or source-level software simulation has been proposed, but existing approaches have largely focused on timing simulation only. In this paper, we propose a novel source-level simulation infrastructure that provides a full range of performance, energy, reliability, power and thermal (PERPT) estimation. Using a fully automated, retargetable back-annotation framework, intermediate representation code is statically annotated with timing, energy and resource accesses information obtained from low-level references at basic block granularity. The annotated model is natively compiled and combined with a cache model and occupancy analyzer to provide target performance, energy, soft-error vulnerability and power estimations. Finally, generated power traces are fed into thermal models for further temperature estimation. Comprehensive evaluations of our source-level models for PERPT estimations are performed. We applied our approach to PowerPC targets running various industry benchmark suites. source-level simulations are evaluated for different PERPT metrics and with cache models at various levels of detail to explore the speed and accuracy tradeoffs. More than 90% accuracy can be achieved for timing, energy, reliability and power estimation, and an average error of 0.05 K exists in steady-state thermal estimation. Simulation speeds range from 180 to 5740 MIPS for different types of metrics at different abstraction levels. [ABSTRACT FROM AUTHOR]

In this paper, a robust model-based battery state-of-charge (SOC) estimating algorithm is proposed with a novel approach based on combination of multimodels data-fusion technique and particle filter (PF). The proposed method is particularly adapted for SOC estimation under real-time conditions and the presence of measurement noise. In this innovative approach, multiple battery models have been used in order to accurately estimate a battery SOC. During the estimation process, the measured battery terminal voltage is compared with the multiple battery models output to generate individual residual, which is then used to calculate the weight of estimated value from each battery model. This weight, which represents the accuracy of observation equation of each battery model, is inversely proportional to the residual. The estimated SOC values from different models are then fused and the weights of estimated values from each battery model are adjusted dynamically using PF and weighted average methodology, in order to calculate the final SOC estimation of the battery. For each proposed battery model, the corresponding parameter-tuning strategies are also presented. In addition, the proposed method has been validated by experimental results. The results demonstrate that the proposed multimodels-based algorithm can be implemented effectively for real-time application, and achieve better accuracy than single model-based methods. [ABSTRACT FROM AUTHOR]

Due to the inconsistent and varied characteristics of lithium-ion battery (LiB) cells, battery pack modeling remains a challenging problem. To model the operation of each cell in the battery pack, considerable work effort and computation time are needed. This paper proposes a data-driven bias-correction-based LiB modeling method, which can significantly reduce the computation work and remain good model accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an effective current model ( ${I}_{\textsf {eff}}$ ) for a near-threshold voltage (NTV) inverter and two-input NAND/NOR gates. The proposed model is validated by 2-D technology computer-aided design mixed-mode device simulations and HSPICE simulations at two different technology nodes. When compared with our model, the existing nominal supply voltage ${I}_{\textsf {eff}}$ models fail to estimate an inverter/NAND/NOR gate performance in the NTV regime. The proposed model is also validated for process, voltage, and temperature variations. Performance variation due to layout-dependent effects (LDEs) and inverse narrow width effect (INWE) are significant in the NTV regime. Therefore, the model is validated while considering LDEs. We show that due to LDEs and INWE, the value of ${I}_{\textsf {eff}}$ per unit width is a function of the device width and the number of fingers. To account for this effect, a systematic methodology is presented to optimize a circuit performance in the NTV domain. An inverter chain with ${C}_{\textsf {out}}/{C}_{\textsf {in}}=\textsf {2000}$ designed employing our methodology results in 3.2%, 35%, and 30% improvement in the delay, energy per operation, and total transistor width, respectively, as compared with existing methodologies. Similarly, data paths resized using the proposed methodology result in a significant performance improvement when compared with their conventionally designed counterparts. [ABSTRACT FROM AUTHOR]

Ever-increasing complexity in high-speed electronic devices and systems presents significant computational challenges in the numerical analysis in terms of desired accuracy, efficiency, and scalable parallelism. The objective of this paper is to investigate high-resolution, high-performance full-wave field solvers for scalable electromagnetic simulations of product-level integrated circuits (ICs) and electronics. The emphasis is placed on advancing parallel algorithms that are provably scalable facilitating a design-through-analysis paradigm, and enabling concurrent multiscale modeling and computation. The capability and the benefits of the algorithms are validated and illustrated through complex 3-D IC and electronics applications. [ABSTRACT FROM PUBLISHER]

Many critical electronic design automation (EDA) problems suffer from the curse of dimensionality, i.e., the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g., 3-D field solvers discretizations and multirate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g., full-chip routing/placement and circuit sizing), or extensive process variations (e.g., variability /reliability analysis and design for manufacturability). The computational challenges generated by such high-dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents “tensor computation” as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage. [ABSTRACT FROM PUBLISHER]

Average-value modeling (AVM) provides an efficient way to study power electronic systems in large- and small-signal senses. This paper presents a new reduced-order AVM for dual-active-bridge dc–dc converters. The proposed model considers the conduction and transformer power losses as well as the input/output filters, which may be very useful for system-level studies. Based on the large-signal AVM, the small-signal model and the control-to-output transfer function are also derived. The proposed AVM is compared with the full-order generalized average model and the detailed model in predicting large-signal transients and small-signal analysis in the frequency domain. The experimental results confirm that the proposed model yields a high accuracy, which represents an improvement over other existing models. [ABSTRACT FROM PUBLISHER]

This paper addresses the generation of accurate and efficient macromodels of high-speed input/output buffers. The proposed modeling approach extends the state-of-the-art methods that are currently available, yielding to a modular and scalable tool for model generation. The modeling procedure applies to both single-ended and differential devices, possibly exhibiting a rich dynamical behavior due to large supply fluctuations or internal voltage regulators. The models are defined by the combination of static surfaces described via compact tensor approximations and linear dynamical state-space relations generated using a robust time-domain vector fitting algorithm. A simple and effective solution is adopted to account for the overclocking operation of output buffer models as well. The feasibility and strength of the proposed method are demonstrated using real devices and complex application test cases for signal and power integrity cosimulations. [ABSTRACT FROM PUBLISHER]

Supercapacitors benefit from unique features including high power density, long cycle life, wide temperature operation range, durability in harsh environments, efficient cycling, and low maintenance cost. This paper presents a validated lumped and computationally efficient electrical and thermal model for a cylindrical supercapacitor cell. The electrical model is a two-state equivalent electric circuit model with three parameters that are identified using temporal experiments. The dependence of the parameters on the state of charge, current direction and magnitude (20–200 A), and temperatures ranging from -40 °C to 60 °C is incorporated in the model. The thermal model is a linear 1-D model with two states. The reversible heat generation which is significant in double-layer capacitors is included in the thermal model. The coupling of the two models enables tuning of the temperature-dependent parameters of the electrical model in real time. The coupled electrothermal model is validated using real-world duty cycles at subzero and room temperatures with root-mean-square errors of (82 mV–87 mV) and (0.17 °C–0.21 °C) for terminal voltage and temperature, respectively. This accurate model is implementable in real-time power applications and also thermal management studies of supercapacitor packs. [ABSTRACT FROM PUBLISHER]

This paper proposes a numerical solution of the nonlinear equations that describes the hysteretic behavior of ferromagnetic materials. From the proper definition and solution of these equations, an equivalent circuit model for a ferromagnetic core is developed and proposed. The results obtained from the equivalent circuit are compared with those obtained by a rigorous numerical solution. [ABSTRACT FROM AUTHOR]

Memristors are promising nanoelectronic devices for the implementation of future AI-driven sensor-processor electronic systems, which are essential for the ongoing digitalization of our world. Accurate and computationally cost-effective models for the manufactured memristors are essential for the design of such systems, especially for the simulation of large circuits. In this brief we address the simplification of the JART memristor model, a generic physics-based model of Valence Change Mechanism (VCM) memristors which accurately describes the dynamic behavior of fabricated memristor devices. Furthermore, the proposed model and simplification methodology have the potential to capture the dynamics of a wide range of memristor devices. Importantly, the implicit description of the current through the memristor is replaced by an explicit mathematical relationship. The proper reproduction of memristor dynamics, verified by applying the system-theoretic Dynamic Route Map (DRM) graphical analysis tool, applicable to first-order systems, can be observed through the proposed simplified model and enables the time-efficient simulation of large arrays of VCM devices. [ABSTRACT FROM AUTHOR]

State-of-health estimates of batteries are essential for onboard electric vehicles in order to provide safe, reliable, and cost-effective battery operation. This paper suggests a method to estimate the 10-s discharge resistance, which is an established battery figure of merit from laboratory testing, without performing the laboratory test. Instead, a state-of-health estimate of batteries is obtained using data directly from their operational use, e.g., onboard electric vehicles. It is shown that simple dynamical battery models, based on a current input and a voltage output, with model parameters dependent on temperature and state of charge, can be derived using AutoRegressive with eXogenous input models, whose order can be adjusted to describe the complex battery behavior. Then, the 10-s discharge resistance can be conveniently computed from the identified model parameters. Moreover, the uncertainty of the estimated resistance values is provided by the method. The suggested method is validated with usage data from emulated electric vehicle operation of an automotive lithium-ion battery cell. The resistance values are estimated accurately for a state-of-charge and temperature range spanning typical electric vehicle operating conditions. The identification of the model parameters and the resistance computation are very fast, rendering the method suitable for onboard application. [ABSTRACT FROM PUBLISHER]

Test generation for hard-to-reach states is important in functional verification. In this paper, we present a path constraint solving-based test generation method (PACOST) which operates in an abstraction-guided semiformal verification framework to cover hard-to-reach states. PACOST combines concrete simulation and symbolic simulation on the design under verification for path constraint extraction and mutation, and uses a sequential path constraint extractor to generate a set of valid input vectors for exploring different simulation paths with different next states. It then works on a target state-oriented abstract model to select the next state with the smallest abstract distance. In addition, the value of register variables in control logic can be controlled by analyzing the data dependence between variables, which helps the simulation converge to the target states. Experimental results show that PACOST can generate shorter traces reaching hard-to-reach states, in comparison with previous abstraction-guided semiformal methods. [ABSTRACT FROM AUTHOR]

This paper deals with a new method of metamodel construction for electromagnetic compatibility (EMC) simulation of power electronic converters. The proposed method uses a compressed wavelet coefficients vector of a time-domain signal and a learning method [support vector regression (SVR)]. This can be applied for a black box approach of modeling of complex systems such as power converters. This allows the study of EMC behavior, the impact of the uncertainties and their propagations inside the model or the design of EMC filters. [ABSTRACT FROM PUBLISHER]

Traditionally, model order reduction methods have been used to reduce the computational complexity of mathematical models of dynamic systems, while preserving their functional characteristics. This technique can also be used to fasten analog circuit simulations without sacrificing their highly nonlinear behavior. In this paper, we present an iterative approach for reducing the computational complexity of nonlinear analog circuits using piecewise linear approximations, $k$ -means clustering, and Krylov space projection techniques. We model primary circuit inputs, design initial conditions, and circuit parameters as fuzzy variables with different distributions in qualitative simulations. We then iteratively fine-tune the reduced models until a model is achieved that meets a predefined performance and accuracy conformance criteria. We demonstrate the effectiveness of our method using several key nonlinear circuits: 1) a transmission line; 2) a ring oscillator; 3) a voltage controlled oscillator; 4) a phase-locked loop; and 5) an analog comparator circuit. Our experiments show that the reduced model simulations are fast and accurate compared with the existing techniques. [ABSTRACT FROM PUBLISHER]

We enhance the capability of industry standard compact model BSIM6 to model the parasitic current $ I_{{\text {edge}}}$ at the shallow trench isolation edge. Accurate, efficient, and scalable model for $ I_{{\text {edge}}}$ is developed by finding the key differences between $ I_{{\text {edge}}}$ and main device drain current ( $ I_{{\text {main}}}$ ). It is found that $ I_{{\text {edge}}}$ has a different sub-threshold slope, body-bias coefficient, and short-channel behavior as compared to $ I_{{\text {main}}}$ . These important effects along with their dependencies on device geometry, bias conditions, and temperature are accounted for in the model. The model is in excellent agreement with experimental data verifying its scalability and readiness for production level usage. [ABSTRACT FROM PUBLISHER]

At a certain level of abstraction, power converters can be represented as linear circuits connected to diodes and controlled electronic switches. The evolutions of the electrical variables are determined by the state-dependent switchings, which complicate the mathematical modeling of controlled power converters. Differently from the complementarity models previously presented in the literature, the model proposed in this paper allows to represent as a linear complementarity system also closed-loop power converters, without requiring the a priori knowledge of the converter modes. A model construction procedure, not dependent on the specific converter topology, is presented. The discretization of the continuous-time model allows to formulate mixed linear complementarity problems for the computation of the control-to-output frequency response and the evolutions of both transient and steady-state currents and voltages. As illustrative examples, Z-source, boost, and buck dc-dc power converters under voltage-mode control and current-mode control operating both in continuous and discontinuous conduction modes are considered. [ABSTRACT FROM AUTHOR]

A new method is introduced for simulation of nonlinear transmission lines based on model-order reduction using the proper-orthogonal decomposition technique. An algorithm is presented for estimating the optimum order of the resulting reduced nonlinear macromodel ensuring its efficiency and accuracy. In contrast to previous publications, the order-estimation algorithm is integrated with the reduction technique leading to significant reduction in the computational cost associated with the generation of the reduced model. [ABSTRACT FROM PUBLISHER]

The modular power electronic transformer (PET) faces difficulty carrying out microsecond-level electromagnetic transient simulations. This article provides a high-speed and high-precision simulation method capable of eliminating the internal nodes and reducing the order of the nodal admittance matrix. Meanwhile, the parallel computing is integrated into the whole solution process, which achieves a significant simulation speedup. A physical prototype is established to prove that the detailed model (DM) is sufficient to reflect the dynamics of physical devices. Moreover, simulations in PSCAD/EMTDC are carried out to compare the proposed method with the DM in terms of accuracy and time efficiency. Simulation results show that the proposed method is accurate to simulate the external and internal dynamics of PET with hundreds of times simulation speed acceleration. [ABSTRACT FROM AUTHOR]

A methodology to create and develop electrical compact models for memristive devices is presented. It comprises a six-step algorithm that uses experimental data of memristive devices in order to obtain a compact model suitable for circuit-level simulations. The model methodology makes use of the definition of the intrinsic variables of memristive systems, to obtain the state map that describes the system, from which the mathematical expressions of the model are obtained. In order to illustrate the usage of the algorithm, a compact memristive model is developed by using experimental data from a TiO2-based resistive switching (RS) device. The resulting mathematical model is compared to the experimental data by computing the mean absolute percentage error (MAPE) between both, which results in 11%, and reduces to 6% after a piecewise approach. The circuit-level representation is implemented using a SPICE macromodel and a Verilog-A module, from which the memristive behavior is also properly reproduced. [ABSTRACT FROM AUTHOR]

Combinatorial optimization problems (COPs) find applications in real-world scientific, industrial, and societal scenarios. Such COPs are computationally NP-hard, and performing an exhaustive brute force search for the optimal solution becomes untenable as the COP size increases. To expedite the COP computation, the Ising model formalism is used, which abstracts spin dynamics in a ferromagnet. The spins are orientated to reach the minimum energy state, representing the optimum COP solution. Previous Ising engine designs utilized dedicated annealing processors or additional digital arithmetic circuits next to the memory bitcells. These custom circuits or processors cannot be repurposed for other applications, incurring significant area and power overhead. In contrast to the prior approaches, this work presents a reconfigurable and scalable compute-within-memory analog approach for Ising computation (called Ising-CIM). This area-efficient approach repurposes existing embedded memory bitcell columns and peripheral circuits to perform analog domain Hamiltonian calculations on the bitlines minimizing area and power overhead significantly. A 13.18-Kb silicon prototype, implemented in a 65-nm CMOS process, demonstrates the Ising-CIM concept and functionality using a 100 $\times $ 64 pixel image in a max-cut COP. The Ising-CIM design achieves 48- $\mu \text{m}~^{\mathrm{ 2}}$ /spin unit spin area and $1091\times $ speedup in annealing time compared to the CPU. [ABSTRACT FROM AUTHOR]

This work advances the application of neural ordinary differential equations (ODEs) to circuit modeling. Prior works primarily utilized the recurrent neural network (RNN), which is a specific type of neural ODE. In this work, the capability of neural ODEs to represent different types of circuits is studied. Stability conditions are presented, both for neural ODEs in a standalone configuration and for neural ODEs with feedback connections, and practical techniques to impose the stability constraints during training are demonstrated. Based on the theoretical and experimental results, this work provides guidance as to when and how an accurate and stable neural ODE circuit model can be generated. [ABSTRACT FROM AUTHOR]

A memristor is a passive circuit element that has numerous applications ranging from storage to processing. The attributes of memristor, such as low power, fast switching speed, and non-volatility, make it a promising candidate for computing applications. To deploy the memristors at the circuit level, a versatile, computationally efficient, and generic model is required to analyze the circuit performance. This article presents a computationally inexpensive, generic, and accurate phenomenological model of the memristor. The proposed model predicts an accurate current–voltage ($I$ – $V$) relationship based on the current conduction mechanism. The presented modeling technique can be incorporated into any practical memristor behavior by optimizing the fitting parameters. The model has the capability to optimize its accuracy and computational efficiency by calibrating the model parameters. The results are compared with the characterization data of numerous memristors and noteworthy models of memristors to validate the proposed approach. The results depict that the proposed model is more flexible, computationally efficient, versatile, and generic. It improves the simulation run time up to 24.47% with the relative root mean squared error of 0.4142%. The model exhibits remarkable results when compared with titanium dioxide-based devices. The proposed model can be deployed to various applications, such as logic design, memory design, and neuromorphic computing. [ABSTRACT FROM AUTHOR]

A computationally efficient simulation framework is set forth in which the semiconductor devices are represented by the physical phenomena relevant to the accurate prediction of high-frequency circuit-level transients and energy losses. Key elements of this framework include an encapsulated diode model and a method of coupling device models with those of external circuit elements given a user-specified SPICE-like netlist. The framework is applied to a single-phase full-bridge diode rectifier circuit with a discussion on time-step requirements and overall computational performance. Comparisons between simulated and measured waveforms are also provided revealing excellent agreement. [ABSTRACT FROM AUTHOR]

In computer architecture research and development, simulation is a powerful way of acquiring and predicting processor behaviors. While architectural simulation has been extensively utilized for computer performance evaluation, design space exploration, and computer architecture assessment, it still suffers from the high computational costs in practice. Specifically, the total simulation time is determined by the simulator’s raw speed and the total number of simulated instructions. The simulator’s speed can be improved by enhanced simulation infrastructures (e.g., simulators with high-level abstraction, parallel simulators, and hardware-assisted simulators). Orthogonal to these work, recent studies also managed to significantly reduce the total number of simulated instructions with a slight loss of accuracy. Interestingly, we observe that most of these work are built upon statistical techniques. This survey presents a comprehensive review to such studies and proposes a taxonomy based on the sources of reduction. In addition to identifying the similarities and differences of state-of-the-art approaches, we further discuss insights gained from these studies as well as implications for future research. [ABSTRACT FROM PUBLISHER]

Multidisciplinary optimization strategies are widely used in the static case and can be extended to a problem with a time-domain model in order to reduce optimization time. The waveform relaxation method is a fixed-point approach applied to waveforms, which allows the coupling of dynamic models. Using the individual discipline feasibility strategy, the coupling is transferred to the optimization problem and leads to a high decrease of the number of model evaluations compared with the multidisciplinary feasibility strategy. The drawback of this approach might be the increased number of optimization variables, but it is coped through an efficient way to compute the derivatives of time-dependent variables. [ABSTRACT FROM AUTHOR]

A highly accurate digital implementation of the Morris-Lecar neuron model is presented with the intended application of hardware acceleration for neuroscience simulation. The novel implementation employs the COordinate Rotation DIgital Computer (CORDIC) algorithm to create a fixed-point implementation that is not only very accurate but requires low digital hardware resources. The accuracy exceeds that of the current state-of-the-art, requires fewer hardware resources to implement, and operates at a higher maximum clock frequency. The design is validated on FPGA and a normalized RMSE of 0.2039 is achieved at a maximum clock frequency of 378.07MHz. [ABSTRACT FROM AUTHOR]

A local constrained multiobjective Bayesian optimization (LoCoMOBO) method is introduced to address automatic sizing and tradeoff exploration for analog and RF integrated circuits (ICs). LoCoMOBO applies to constrained optimization problems utilizing multiple Gaussian process (GP) models that approximate the objective and constraint functions locally in the search space. It searches for potential pareto optimal solutions within trust regions of the search space using only a few time-consuming simulations. The trust regions are adaptively updated during the optimization process based on feasibility and hypervolume metrics. In contrast to mainstream Bayesian optimization approaches, LoCoMOBO uses a new acquisition function that can provide multiple query points, therefore allowing for parallel execution of costly simulations. GP inference is also enhanced by using GPU acceleration in order to handle highly constrained problems that require large sample budgets. Combined with a framework for schematic parametrization and simulator calls, LoCoMOBO provides improved performance tradeoffs and sizing results on three real-world circuit examples, while reducing the total runtime up to $\times 43$ times compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

After its recent improvements described here, open source finite element software Elmer is shown to be a highly efficient option for electrical machine modeling. The parallelization of computational burden is shown to be a necessity. The methods implemented enable applying fully parallel computation to electrical machine models, including rotation and electrical circuits. Computational experiments performed demonstrate that Elmer can effectively utilize several hundreds of computational cores in parallel, making it an attractive alternative when computational speed is of high importance. [ABSTRACT FROM PUBLISHER]

Nonlinear phenomena in electrical contacts deteriorate the quality of communication systems with the production of passive intermodulation (PIM). The theoretical evaluation of PIM as a function of the physical parameters of the contact is rather complicated. Standard linear and macroscopic contact models do not take into account all microscopic aspects of the contact responsible for its nonlinear behavior. For the above reason, an accurate analysis of the PIM should be carried out by using microscopic contact models, defined by dozens of parameters, some of which cannot be precisely measured or estimated. This article presents a statistical analysis of the PIM level by taking into account a possible uncertain interval for the physical parameters of the contact. Such statistical interpretation is then used in order to identify the most relevant physical parameters for the PIM generation via a sensitivity analysis, through the use of a surrogate model that speeds up the huge amount of PIM computations. The results of the sensitivity analysis allow us to build a simpler model depending only on few dominant parameters. [ABSTRACT FROM AUTHOR]

Research on the taste conduction mechanisms of data processing by the electronic tongue (e-tongue) is lacking. These mechanisms affect taste substance identification by the e-tongue system. This work proposed a computational model of taste pathways based on nerve conduction mechanisms to identify taste substances. This work modelled human taste pathways based on the Katchalsky models and validated the rationality of the modelling forms of the critical pathway structures. Next, this work simulated the response status and power spectral density of the model nodes and verified the bionic performance of the computational model. The results showed, first, that the computational model described the dynamic characteristics of the taste pathways. Second, the phase diagrams of the pathway structures revealed chaotic characteristics. This result showed that the modelling forms were rational. Third, when stimulated, the model nodes exhibited fast responses. During stimulation, the power spectral density of the model nodes demonstrated 1/ ${f}$ characteristics. The fast-response ability and 1/ ${f}$ characteristics reflected the bionic performance of the computational model. Finally, the computational model was used to identify beer, tea, and apple samples. Compared with other classification methods, the computational model achieved better classification results of 93.33%, 94.00%, and 76.67%, the best specificity of 0.9833, 0.9850, and 0.9417, and the best Kappa coefficients of 0.9180, 0.9261, and 0.7245 for the identification of the beer, tea, and apple samples, respectively. In conclusion, the computational model can effectively identify taste substances. [ABSTRACT FROM AUTHOR]

In the energy storage field, supercapacitors (SCs) are gaining more and more attention thanks to features, such as high-power density, high life cycles, and lack of maintenance. In this article, an improved SC three-branch model, which considers the residual charge phenomenon, is presented. The procedure to estimate the model parameters and the related experimental setup are presented. The parameter estimation procedure is repeated for several SCs of the same type. The average parameters are, then, obtained and used as initial guesses for a recursive least squares optimization algorithm to increase the accuracy of the model. The model of a single SC is, then, extended to SC banks, testing different configurations and operating conditions. The simulation results, obtained in the MATLAB/Simulink environment for both the single SC and the different SC bank configurations, are validated with experimental tests to assess the model accuracy. [ABSTRACT FROM AUTHOR]

The extraction of the model parameters is as important as the development of compact model itself because simulation accuracy is fully determined by the accuracy of the parameters used. This article proposes an efficient model-parameter extraction method for compact models of power metal-oxide semiconductor field-effect transistors (mosfets). The proposed method employs automatic differentiation (AD), which is extensively used for training artificial neural networks. In the proposed AD-based parameter extraction, gradient of all the model parameters is analytically calculated by forming a graph that facilitates the backward propagation of errors. Based on the calculated gradient, computationally intensive numerical differentiation is eliminated and the model parameters are efficiently optimized. Experiments are conducted to fit current and capacitance characteristics of commercially available silicon carbide mosfet using power mosfet models having 13 model parameters. Results demonstrated that the proposed method could successfully derive the model parameters 3.50× faster than a conventional numerical-differentiation method while achieving the equal accuracy. [ABSTRACT FROM AUTHOR]

Model checkers provide algorithms for proving that a mathematical model of a system satisfies a given specification. In case of a violation, a counterexample that shows the erroneous behavior is returned. Understanding these counterexamples is challenging, especially for hyperproperty specifications, i.e., specifications that relate multiple executions of a system to each other. We aim to facilitate the visual analysis of such counterexamples through our HyperVis tool, which provides interactive visualizations of the given model, specification, and counterexample. Within an iterative and interdisciplinary design process, we developed visualization solutions that can effectively communicate the core aspects of the model checking result. Specifically, we introduce graphical representations of binary values for improving pattern recognition, color encoding for better indicating related aspects, visually enhanced textual descriptions, as well as extensive cross-view highlighting mechanisms. Further, through an underlying causal analysis of the counterexample, we are also able to identify values that contributed to the violation and use this knowledge for both improved encoding and highlighting. Finally, the analyst can modify both the specification of the hyperproperty and the system directly within HyperVis and initiate the model checking of the new version. In combination, these features notably support the analyst in understanding the error leading to the counterexample as well as iterating the provided system and specification. We ran multiple case studies with HyperVis and tested it with domain experts in qualitative feedback sessions. The participants' positive feedback confirms the considerable improvement over the manual, text-based status quo and the value of the tool for explaining hyperproperties. [ABSTRACT FROM AUTHOR]

A band-limited magnitude-selective affine (MSA) function-based model is proposed for digital predistortion (DPD) of broadband power amplifiers (PAs) in 5G millimeter-wave frequency band. The new model is derived from the band-limited canonical piecewise linear function-based model by employing MSA functions, which can obviously reduce the number of multiplication operations and the model complexity. Experimental results show that the proposed model gives better normalized mean square error and DPD performance in comparison with some existing models when a broadband PA is excited by a 5G new radio (NR) signal of 400-MHz bandwidth at 28-GHz band. [ABSTRACT FROM AUTHOR]

To obtain a full exploitation of battery potential in energy storage applications, an accurate modeling of electrochemical batteries is needed. In real terms, an accurate knowledge of state of charge (SOC) and state of health (SOH) of the battery pack is needed to allow a precise design of the control algorithms for energy storage systems (ESSs). Initially, a review of effective methods for SOC and SOH assessment has been performed with the aim to analyze pros and cons of standard methods. Then, as the tradeoff between accuracy and complexity of the model is the major concern, a novel technique for SOC and SOH estimation has been proposed. It is based on the development of a battery circuit model and on a procedure for setting the model parameters. Such a procedure performs a real-time comparison between measured and calculated values of the battery voltage while a PI-based observer is used to provide the SOC and SOH actual values. This ensures a good accuracy in a wide range of operating conditions. Moreover, a simple start-up identification process is required based on battery data-sheet exploitation. Because of the low computational burden of the whole algorithm, it can be easily implemented in low-cost control units. An experimental comparison between SOC and SOH estimation performed by suggested and standard methods is able to confirm the consistency of the proposed approach. [ABSTRACT FROM AUTHOR]